

import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer

class        a__    (       snake_case_      ):
			"""simple docstring"""







			def   UpperCamelCase		(     self							)	->    Dict:
							'''simple docstring'''






							A__          =      tempfile.mkdtemp()
							A__          =      5

							# Realm tok
							A__          =      [
							    "[UNK]",
							    "[CLS]",
							    "[SEP]",
							    "[PAD]",
							    "[MASK]",
							    "test",
							    "question",
							    "this",
							    "is",
							    "the",
							    "first",
							    "second",
							    "third",
							    "fourth",
							    "fifth",
							    "record",
							    "want",
							    "##want",
							    "##ed",
							    "wa",
							    "un",
							    "runn",
							    "##ing",
							    ",",
							    "low",
							    "lowest",
							]
							A__          =      os.path.join(self.tmpdirname   ,   "realm_tokenizer"							)
							os.makedirs(lowercase   ,   exist_ok=lowercase							)
							A__          =      os.path.join(lowercase   ,   VOCAB_FILES_NAMES["vocab_file"]							)
							with open(self.vocab_file   ,   "w"   ,   encoding="utf-8"							) as vocab_writer:
											vocab_writer.write("".join([x + "\n" for x in vocab_tokens]							)							)

							A__          =      os.path.join(self.tmpdirname   ,   "realm_block_records"							)
							os.makedirs(lowercase   ,   exist_ok=lowercase							)


			def   UpperCamelCase		(     self							)	->    List[str]:
							'''simple docstring'''






							return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,   "realm_tokenizer"							)							)


			def   UpperCamelCase		(     self							)	->    Optional[int]:
							'''simple docstring'''






							shutil.rmtree(self.tmpdirname							)


			def   UpperCamelCase		(     self							)	->    str:
							'''simple docstring'''






							A__          =      RealmConfig(num_block_records=self.num_block_records							)
							return config


			def   UpperCamelCase		(     self							)	->    Union[str, Any]:
							'''simple docstring'''






							A__          =      Dataset.from_dict(
							    {
							        "id": ["0", "1"],
							        "question": ["foo", "bar"],
							        "answers": [["Foo", "Bar"], ["Bar"]],
							    }							)
							return dataset


			def   UpperCamelCase		(     self							)	->    List[Any]:
							'''simple docstring'''






							A__          =      np.array(
							    [
							        b"This is the first record",
							        b"This is the second record",
							        b"This is the third record",
							        b"This is the fourth record",
							        b"This is the fifth record",
							        b"This is a longer longer longer record",
							    ]   ,   dtype=lowercase   ,   )
							return block_records


			def   UpperCamelCase		(     self							)	->    Any:
							'''simple docstring'''






							A__          =      RealmRetriever(
							    block_records=self.get_dummy_block_records()   ,   tokenizer=self.get_tokenizer()   ,   )
							return retriever


			def   UpperCamelCase		(     self							)	->    Any:
							'''simple docstring'''






							A__          =      self.get_config()
							A__          =      self.get_dummy_retriever()
							A__          =      retriever.tokenizer

							A__          =      np.array([0, 3]   ,   dtype="long"							)
							A__          =      tokenizer(["Test question"]							).input_ids
							A__          =      tokenizer(
							    ["the fourth"]   ,   add_special_tokens=lowercase   ,   return_token_type_ids=lowercase   ,   return_attention_mask=lowercase   ,   ).input_ids
							A__          =      config.reader_seq_len

							A__     ,					A__     ,					A__     ,					A__          =      retriever(
							    lowercase   ,   lowercase   ,   answer_ids=lowercase   ,   max_length=lowercase   ,   return_tensors="np"							)

							self.assertEqual(len(lowercase							)   ,   2							)
							self.assertEqual(len(lowercase							)   ,   2							)
							self.assertEqual(len(lowercase							)   ,   2							)
							self.assertEqual(concat_inputs.input_ids.shape   ,   (2, 10)							)
							self.assertEqual(concat_inputs.attention_mask.shape   ,   (2, 10)							)
							self.assertEqual(concat_inputs.token_type_ids.shape   ,   (2, 10)							)
							self.assertEqual(concat_inputs.special_tokens_mask.shape   ,   (2, 10)							)
							self.assertEqual(
							    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]							)   ,   ["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "first", "record", "[SEP]"]   ,   )
							self.assertEqual(
							    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]							)   ,   ["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "fourth", "record", "[SEP]"]   ,   )


			def   UpperCamelCase		(     self							)	->    Union[str, Any]:
							'''simple docstring'''






							A__          =      self.get_config()
							A__          =      self.get_dummy_retriever()
							A__          =      retriever.tokenizer

							A__          =      np.array([0, 3, 5]   ,   dtype="long"							)
							A__          =      tokenizer(["Test question"]							).input_ids
							A__          =      tokenizer(
							    ["the fourth", "longer longer"]   ,   add_special_tokens=lowercase   ,   return_token_type_ids=lowercase   ,   return_attention_mask=lowercase   ,   ).input_ids
							A__          =      config.reader_seq_len

							A__     ,					A__     ,					A__     ,					A__          =      retriever(
							    lowercase   ,   lowercase   ,   answer_ids=lowercase   ,   max_length=lowercase   ,   return_tensors="np"							)

							self.assertEqual([False, True, True]   ,   lowercase							)
							self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]   ,   lowercase							)
							self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]   ,   lowercase							)


			def   UpperCamelCase		(     self							)	->    Optional[int]:
							'''simple docstring'''






							A__          =      self.get_dummy_retriever()
							retriever.save_pretrained(os.path.join(self.tmpdirname   ,   "realm_block_records"							)							)

							# Test local path
							A__          =      retriever.from_pretrained(os.path.join(self.tmpdirname   ,   "realm_block_records"							)							)
							self.assertEqual(retriever.block_records[0]   ,   b"This is the first record"							)

							# Test mocked remote path
							with patch("transformers.models.realm.retrieval_realm.hf_hub_download"							) as mock_hf_hub_download:
											A__          =      os.path.join(
											    os.path.join(self.tmpdirname   ,   "realm_block_records"							)   ,   _REALM_BLOCK_RECORDS_FILENAME							)
											A__          =      RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa"							)

							self.assertEqual(retriever.block_records[0]   ,   b"This is the first record"							)


"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
   import PIL

SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)



def 	_lowerCamelCase(	a				):
       if isinstance(a   ,    (list, tuple)				) and isinstance(videos[0]   ,    (list, tuple)				) and is_valid_image(videos[0][0]				):
              return videos

       elif isinstance(a   ,    (list, tuple)				) and is_valid_image(videos[0]				):
              return [videos]

       elif is_valid_image(a				):
              return [[videos]]

       raise ValueError(F"Could not make batched video from {videos}"				)






class  snake_case__      (       snake_case_       ):
 _snake_case       :					List[Any]							  =						["""pixel_values"""]


 def __init__( self    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = PILImageResampling.BILINEAR    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = True    ,		lowerCamelCase = 1 / 255    ,		lowerCamelCase = True    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase						)
        __a       =    size if size is not None else {"shortest_edge": 256}
        __a       =    get_size_dict(lowerCamelCase    ,		default_to_square=lowerCamelCase						)
        __a       =    crop_size if crop_size is not None else {"height": 224, "width": 224}
        __a       =    get_size_dict(lowerCamelCase    ,		param_name="crop_size"						)

        __a       =    do_resize
        __a       =    size
        __a       =    do_center_crop
        __a       =    crop_size
        __a       =    resample
        __a       =    do_rescale
        __a       =    rescale_factor
        __a       =    offset
        __a       =    do_normalize
        __a       =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __a       =    image_std if image_std is not None else IMAGENET_STANDARD_STD


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = PILImageResampling.BILINEAR    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    get_size_dict(lowerCamelCase    ,		default_to_square=lowerCamelCase						)
        if "shortest_edge" in size:
               __a       =    get_resize_output_image_size(lowerCamelCase    ,		size["shortest_edge"]    ,		default_to_square=lowerCamelCase						)
        elif "height" in size and "width" in size:
               __a       =    (size["height"], size["width"])
        else:
               raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"						)
        return resize(lowerCamelCase    ,		size=lowerCamelCase    ,		resample=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    get_size_dict(lowerCamelCase						)
        if "height" not in size or "width" not in size:
               raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"						)
        return center_crop(lowerCamelCase    ,		size=(size["height"], size["width"])    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    image.astype(np.floataa						)
        if offset:
               __a       =    image - (scale / 2)
        return rescale(lowerCamelCase    ,		scale=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        return normalize(lowerCamelCase    ,		mean=lowerCamelCase    ,		std=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = ChannelDimension.FIRST    ,		):
        if do_resize and size is None or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."						)

        if do_center_crop and crop_size is None:
               raise ValueError("Crop size must be specified if do_center_crop is True."						)

        if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."						)

        if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("Image mean and std must be specified if do_normalize is True."						)

        if offset and not do_rescale:
               raise ValueError("For offset, do_rescale must also be set to True."						)

        # All transformations expect numpy arrays.
        __a       =    to_numpy_array(lowerCamelCase						)

        if do_resize:
               __a       =    self.resize(image=lowerCamelCase    ,		size=lowerCamelCase    ,		resample=lowerCamelCase						)

        if do_center_crop:
               __a       =    self.center_crop(lowerCamelCase    ,		size=lowerCamelCase						)

        if do_rescale:
               __a       =    self.rescale(image=lowerCamelCase    ,		scale=lowerCamelCase    ,		offset=lowerCamelCase						)

        if do_normalize:
               __a       =    self.normalize(image=lowerCamelCase    ,		mean=lowerCamelCase    ,		std=lowerCamelCase						)

        __a       =    to_channel_dimension_format(lowerCamelCase    ,		lowerCamelCase						)
        return image






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = ChannelDimension.FIRST    ,		**lowerCamelCase    ,		):
        __a       =    do_resize if do_resize is not None else self.do_resize
        __a       =    resample if resample is not None else self.resample
        __a       =    do_center_crop if do_center_crop is not None else self.do_center_crop
        __a       =    do_rescale if do_rescale is not None else self.do_rescale
        __a       =    rescale_factor if rescale_factor is not None else self.rescale_factor
        __a       =    offset if offset is not None else self.offset
        __a       =    do_normalize if do_normalize is not None else self.do_normalize
        __a       =    image_mean if image_mean is not None else self.image_mean
        __a       =    image_std if image_std is not None else self.image_std

        __a       =    size if size is not None else self.size
        __a       =    get_size_dict(lowerCamelCase    ,		default_to_square=lowerCamelCase						)
        __a       =    crop_size if crop_size is not None else self.crop_size
        __a       =    get_size_dict(lowerCamelCase    ,		param_name="crop_size"						)

        if not valid_images(lowerCamelCase						):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."						)

        __a       =    make_batched(lowerCamelCase						)

        __a       =    [
            [
                self._preprocess_image(
                    image=lowerCamelCase    ,		do_resize=lowerCamelCase    ,		size=lowerCamelCase    ,		resample=lowerCamelCase    ,		do_center_crop=lowerCamelCase    ,		crop_size=lowerCamelCase    ,		do_rescale=lowerCamelCase    ,		rescale_factor=lowerCamelCase    ,		offset=lowerCamelCase    ,		do_normalize=lowerCamelCase    ,		image_mean=lowerCamelCase    ,		image_std=lowerCamelCase    ,		data_format=lowerCamelCase    ,		)
                for img in video
            ]
            for video in videos
        ]

        __a       =    {"pixel_values": videos}
        return BatchFeature(data=lowerCamelCase    ,		tensor_type=lowerCamelCase						)
